Measures of Central Tendency, Dispersion and Skewness

With examples in R

Introduction

Central tendency measures help to understand where the data cluster around, providing a typical value of the dataset. Dispersion measures provide insights into the spread or variability of the dataset, helping to assess the degree of heterogeneity. Skewness measures the departure from symmetry in the distribution, indicating whether the data is concentrated on one side or stretched out on the other.

Measures of Central Tendency

Measures of central tendency are statistical tools used to summarize the central or typical value of a dataset. They provide valuable insights into the distribution of data points and are crucial for understanding the central tendency or average behavior within a dataset. In this article, we will explore various measures of central tendency along with their interpretation and implementation using the R programming language.

Mean

The mean, also known as the arithmetic average, is the most common measure of central tendency. It is calculated by summing up all the values in the dataset and dividing by the total number of observations.

dataset <- c(10, 15, 20, 25, 30)
mean_value <- mean(dataset)